Comment on ChatGPT is down worldwide, conversations disappeared for users

• pahlimur@lemmy.world ⁨3⁩ ⁨weeks⁩ ago

  It’s not easy to solve because its not possible to solve. ML has been around since before computers, it’s not magically going to get efficient. The models are already optimized.

  Revenue isn’t profit. These companies are the biggest cost sinks ever.

  Heating a single building is a joke marketing tactic compared to the actual energy impact these LLM energy sinks have.

  I’m an automation engineer, LLMs suck at anything cutting edge. Its basically a mainstream knowledge reproducer with no original outputs. Meaning it can’t do anything that isnt already done.

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  • NotMyOldRedditName@lemmy.world ⁨3⁩ ⁨weeks⁩ ago

    Why on earth do you think things can’t be optimized on the LLM level?

    There are constant improvements being made there, they are not in any way shape or form fully optimized yet. Go follow the /r/LocalLlama sub for example and there’s constant breakthroughs happening, and then a few months later you see a LLM utilizing them come out, and they’re suddenly smaller, or you can run a large model on smaller memory footprint, or you can get a larger context on the same hardware etc.

    This is all so fucking early, to be so naive to think that they’re as optimized as they can get is hilarious.

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    • pahlimur@lemmy.world ⁨3⁩ ⁨weeks⁩ ago

      I’ll take a step back. These LLM models are interesting. They are being trained in interesting new ways. They are becoming more ‘accurate’, I guess. ‘Accuracy’ is very subjective and can be manipulated.

      Machine learning is still the same though.

      LLMs still will never expand beyond their inputs.

      My point is it’s not early anymore. We are near or past the peak of LLM development. The extreme amount of resources being thrown at it is the sign that we are near the end.

      That sub should not be used to justify anything, just like any subreddit at any point in time.

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      • NotMyOldRedditName@lemmy.world ⁨3⁩ ⁨weeks⁩ ago

        My point is it’s not early anymore. We are near or past the peak of LLM development.

        I think we’re just going to have to agree to disagree on this part.

        I’ll agree though that IF what you’re saying is true, then they won’t succeed.

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• redwattlebird@lemmings.world ⁨3⁩ ⁨weeks⁩ ago

  These companies have BILLIONS in revenue and millions of customers, and you’re saying very few want to pay…

  Yep, I am. Just follow the money. Here’s an example:

  https://www.theregister.com/2025/10/29/microsoft_earnings_q1_26_openai_loss/

  not saying this is an easy problem to solve, but you’re making it sound no one wants it and they can never do it.

  … That’s all in your head, mate. I never said that nor did I imply it.

  What I am implying is that the uptake is so small compared to the investment that it is unlikely to turn a profit.

  If OpenAI can build a datacenter that re-uses all it’s heat for example to heat a hospital nearby, that’s another step towards reaching profitability.

  😐

  I’ve worked in the building industry for over 20 years. This is simply not feasible both from a material standpoint and physics standpoint.

  I know it’s an example, but this kind of rhetoric is exactly the kind of wishful thinking that I see in so many people who want LLMs to be a main staple of our everyday lives. Scratch the surface and it’s all just fantasy.

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  • NotMyOldRedditName@lemmy.world ⁨3⁩ ⁨weeks⁩ ago

    You > the trends show that very few want to pay for this service.

    Me > These companies have BILLIONS in revenue and millions of customers, and you’re saying very few want to pay

    Me > … but you’re making it sound no one wants it

    You > … That’s all in your head, mate. I never said that nor did I imply it.

    Pretty sure it’s not all in my head.

    The heat example was just one small example of things these large data centers (not just AI ones) can do to help lower costs, and they are a real thing that are being considered. It’s not a solution their power hungry needs, but it is a small step forward on how we can do things better.

    www.bbc.com/news/articles/cew4080092eo

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    • redwattlebird@lemmings.world ⁨3⁩ ⁨weeks⁩ ago

      And how, pray tell, will doing all of that return a profit?

      I’m from Australia, so I can only speak to the Australian climate and industry. I can confidently say that the model shown in Vienna is not feasible in our country. We simply don’t have much use for excess heat and we are highly susceptible to droughts. DCs use a lot of water to cool down and having these all over the country for private enterprise is bonkers. So, that’s instantly a market that isn’t profitable. Furthermore, it’s not feasible to build a pipe and re-route the heat across large distances with minimal heat loss.

      However, even when or if they implement this throughout all of Austria, it won’t return a profit (which is what I thought your attachment was here, not the feasibility. We are talking about profitability, right?). This project cost $3.5m Euro and partially funded by tax. It’s not a great example of profitability but a good example of sustainability measures.

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      • NotMyOldRedditName@lemmy.world ⁨3⁩ ⁨weeks⁩ ago

        Australia isn’t the greatest spot to run a data centre in general in terms of heat, but I do understand the need for sovereign data centres, so this obviously can’t work everywhere.

        What makes you think $3.5 million can’t be profitable? A mid sized hospitals heating bill can get into the many hundreds of thousands or into the millions even. Especially if it’s in a colder environment. A 5-6 year payback on that wouldn’t be terrible.

        These colder locations are the ideal locations for the data centres in the first place because they generally want a cooler climate to begin with.

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• leftist_lawyer@lemmy.today ⁨3⁩ ⁨weeks⁩ ago

  Pretty obvious you’re not reading Zitron et al.

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• leftist_lawyer@lemmy.today ⁨3⁩ ⁨weeks⁩ ago

  just need to optimize. Like they haven’t just been trying for years already with, again, incredibly marginal returns that continue to diminish with each optimization.

  Derp.

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